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Project Description
Various methods have been devised for assessing the correctness of a Causal Model given a dataset thought to be produced by that model. This project will survey the available methods and assess them to determine the most powerful and practical methods. The project will result in one or more of the most promising methods being implemented within the HPCC Causality Framework.
The work involves developing test cases and comparing results using various in-house and publicly known validation methods. The student will design tests, perform tests, and document their results. Assessment of validation methods will be both qualitative and quantitative, and will include run-time performance as well as accuracy.
The successful candidate should have a background in mathematics and statistics, machine learning, and preferably knowledge of Causal Science, Causal algorithms and Causal analysis packages.
If you are interested in this project, please contact the mentor shown below.
More information about the HPCC Systems Causality Toolkit is available in our blog Causality 2021.
Mentor | Roger Dev Backup Mentor: Lili Xu |
Skills needed |
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Other resources |
Student Posters from previous Causality Projects:
Student Blog Journals |